This section is intended to provide a background or context to the invention disclosed below. The description herein may include concepts that could be pursued, but are not necessarily ones that have been previously conceived, implemented or described. Therefore, unless otherwise explicitly indicated herein, what is described in this section is not prior art to the description in this application and is not admitted to be prior art by inclusion in this section. Abbreviations that may be found in the specification and/or the drawing figures are defined at the end of this document prior to the claims.
In terms of network assisted interference cancelation and suppression (NAICS), several different advanced receiver structures may be envisioned. These different receiver classes included receivers based on interference rejection combining (IRC receivers), receivers performing interference cancelation (IC receivers) as well as receivers decoding using the maximum likelihood principles (ML receivers). IRC receivers might include classical linear MMSE-IRC receivers as well as enhanced versions based on the same MMSE-IRC principles. The class of interference cancelation (IC) type or receivers includes, beside others, symbol level IC receivers (SLIC) as well as codeword type of IC receivers (CWIC) where the cancelation can be done using linear cancelation techniques (L-CWIC) or alternatively using ML principles in the interference cancelation stage (ML-CWIC). The interference cancelation in general can be performed through serial interference cancelation (SIC), parallel interference cancellation (PIC) as well as using iterative methods (Iterative IC). Finally, the class of ML type of joint receivers may include, besides its basic operation, some reduced complexity implementations (R-ML), iterative ML decoding (Iterative-ML) as well as a combination of iterative processing including some reduced complexity search functions (Iterative-R-ML).
It is noted that while interference suppression/rejection type receivers can be used alone in the NAICS operation, they can be also used as a component receiver for more complicated receivers in NAICS. For example, L-CWIC can use an interference suppression type receiver such as E-LMMSE-IRC to first demodulate a dominant interference signal before any type of IC operation, and then subtract the regenerated dominant interference signal from the received signal, the resulted signal is again passed to an interference suppression type receiver such as E-LMMSE-IRC to demodulate the desired signal and interference in this case constitutes of interference other than the dominant interference and the residue of cancelled dominant interference. Hence techniques which enhance the performance of interference suppression type receivers can also enhance the performance of some interference cancellation type receivers.
In general, the receiver model can be formulated as follows:rt,f=ht,f,1x1+ht,f,2x2+ . . . +ht,f,MxM+n. Where ht,f,1 is channel response for the desired signal, x1 is the PDSCH for the victim UE (the UE of interest); ht,f,2, . . . , ht,f,M are the channel responses for interference, n is the thermal noise, t is the OFDM symbol index, f is the tone index; and rt,f is the received signal at the victim UE. Here “channel response” is understood to be the composition of the air channel and precoding matrix applied to the PDSCH transmission, and a “tone index” is an index for subchannels.
Let us first consider linear, interference suppression/rejection type of receivers. From estimation theory, the MMSE estimator for the linear receiver weight of an OFDM signal is given by the following:Wt,f,1={circumflex over (R)}t,f,in−1ĥt,f,1H,where AH is the Hermitian of A, {circumflex over (R)}in is the sample covariance matrix for interference and noise, and ĥt,f,1 is the channel estimate at (t, f) for the desired signal x1. And the estimate of x1 is given by{circumflex over (x)}1=Wt,f,1rt,f.
Based on the above formula, the key factor impacting the performance of receiver-based linear interference rejection/suppression is to have interference estimation. The same thing holds for the other receiver types including ML and IC type of receivers. The better the estimate of the interference can be performed, the better the final decoding performance of the desired signal in the end will be. There are several ways to perform the interference estimation itself as well as channel estimation for the interfering transmitted signals. Below we consider different example approaches on how to derive the spatial covariance matrix of the interference to be used by linear MMSE-IRC type of receivers.
In a first approach (Approach 1), the following are performed:                First estimate the channel response for the desired signal from DMRS REs; and        Compute the total received signal covariance matrix and take out the channel estimation results from desired signals to determine Rt,f,in.        
A second approach (Approach 2) involves estimating the top m channel responses in terms of receiver power from DMRS REs, ht,f,1, ht,f,m, as follows:
      R          t      ,      f      ,      in        =                    ∑                  k          =          2                m            ⁢                        h                      t            ,            f            ,            k                          ⁢                  h                      t            ,            f            ,            k                    H                      +                  σ        n            ⁢              I        .            The channel estimates themselves derived in this second approach can be used also in ML and IC type of receiver operation directly.
A third approach (Approach 3) is through the well-known “sample matrix inversion” principle from the PDSCH REs as such. The sample matrix inversion is an algorithm that estimates weights of an array (adaptive filter) by replacing the correlation matrix R, with its estimate. Using K samples x(k), k=1, 2, . .. , K, an unbiased estimate of RU, the correlation matrix of the array signals, may be obtained by means of a simple averaging scheme:
                    R        ^            U        ⁡          (      k      )        =            1      /      k        ⁢          ∑                        (                                    x              ⁡                              (                k                )                                      ⁢                                          x                H                            ⁡                              (                k                )                                              )                .            
The expression of the theoretically optimal weights requires the inverse of RU, and the inverse of the estimates matrix is then used for finding estimated optimal weights.
Therefore, the spatial correlation estimate will be given by using the n PDSCH RE samples available for a certain time/frequency point:
            R      ^              t      ,      f      ,      in        =            ∑              n        =        1            N        ⁢                            r                      t            ,            f                          ⁡                  [          n          ]                    ⁢                                    r                          t              ,              f                        H                    ⁡                      [            n            ]                          .            
There are challenges for all three approaches. For instance, looking at Approaches 1 and 2, as despreading with [+1+1] or [+1−1] is used in the channel estimation of ht,f,n1, and another DMRS (with channel response ht,f,n2) is also spread with the same pattern, then suppression of the contribution from ht,f,n2 in the estimate of ht,f,n1 is not really achieved.
With the second approach, joint estimation of ht,f,1, . . . , ht,f,m may be necessary in the case some of these components have similar power level to achieve good channel estimation quality of the different interference sources. Well performed successive channel estimation depends on good estimates of the power profile of ht,f,1, . . . , ht,f,m. It can be envisioned that a UE can be assisted by the network with the following information                the DMRS seeds; and        The number of DMRS ports with each seed.Besides, for the cases that interference reception power is much lower than the power of the UE's own signals, it is very difficult for the UE to derive a channel estimate/covariance matrix estimate of a particular interference source. As mentioned above, the channel estimates derived this way may not only be used by linear interference rejection/suppression type of receiver combining pre-filters, but may be used as well for interference cancelation and ML type of receiver processing.        
With the third approach, there is actually no real issue in terms of having any DMRS or sequence limitations, but the MMSE performance relies on good channel estimation quality as such, as in the inversion of the MMSE—the same channel is present in the nominator and denominator. Therefore, channel estimation errors have a rather tremendous effect on performance deterioration of the IRC type of receiver.
It is also noted for maximum likelihood type receivers, typically a pre-whitening step is taken on the received signal, so contribution to the processed received signal from signals other than the desired signal and dominant interference signals is spatially white, which facilitates the application of advanced techniques in estimation theory or detection theory, as many of these techniques assume spatially white noise. For a pre-whitening step, see, e.g., MMSE-Prewhitened-MLD Equalizer for MIMO DFT-Precoded-OFDMA” by Kiran Kuchi, pp.328-331, IEEE Wireless Communications Letters, VOL. 1, NO. 4, August 2012. It is seen that the interference estimation, especially the interference covariance matrix estimation, is also important for maximum likelihood type receivers.
Now the importance of interference estimation is demonstrated for all the receiver types considered for NAICS (interference rejection, interference cancellation, maximum likelihood), and in the following the interference rejection (or interference suppression) type receiver and L-WCIC are used as illustrative examples.
In Rel-11, IMR (Interference Measurement Resource) is defined to facilitate the interference measurement for channel quality indication (CQI) calculation to support coordinated multi-point transmission (CoMP). Therefore, IMR REs could be used for interference covariance matrix estimation. However this option has some drawbacks:
1) The IMR duty cycle is at least 5 ms, which is not helpful for interference covariance matrix estimation since interference changes quickly from one 1 ms subframe to the next. Therefore, an estimate obtained in a certain subframe might not be valid as such even for the following downlink subframe. Therefore, such resources would need to be present in each subframe in order to help the receiver decoding operation.
2) Each IMR is of 4 REs in a physical resource block (PRB), but interference covariance matrix estimation may require a larger number of samples/REs in order to achieve a good estimation and as a consequence decoding performance.
3) Each IMR is of uniform density in all PRBs and the IMR pattern is the same across PRBs;
4) Each IMR RE is free of PDSCH transmissions from a single cell (including all potential spatial layers) in that PRB.
Thus, it can be seen that improvements could be made in this area.